AbstractApplied to microarray data, Nonnegative Matrix Factorization
(NMF) can be viewed as a generalized clustering algorithm allowing for
gene overlaps - an important feature in this domain where genes can be
involved in several biological processes. In this paper we present siNMF,
a generalization of NMF that can simultaneously factorize a gene
expression matrix and a matrix of transcription regulatory
influences. Thus, siNMF constructs gene clusters taking into account not
just expression information, but also background knowledge on potential
regulatory factors of the clusters. A preliminary application of the
algorithm to a real-life pancreatic cancer dataset shows the feasibility
of our approach.